How AI is Driving Marketing Software 2.0
Andrej Karpathy knows a thing or two about AI, neural networks and machine learning. And what he says about how they’re affecting the future of software development holds true for B2B marketing and ABM, too.
If you don’t know his name, you’ve still probably heard of (or even experienced) his work, which is regularly trumpeted all over the media: he’s the Director of AI at Tesla. He and his team have been faced with hugely complex challenges in attempting to deliver truly autonomous mobility, and there’s been copious media scrutiny along with it.
Along the way, he’s applied a lot of expertise in neural networks to the task, and it’s his opinion that we’re facing a paradigm shift in how software is developed. Software 2.0, as he describes it, will be largely written by neural networks, not human developers.
It’s happening even today, as us slow, fleshy types provide a neural network with a series of constraints (a continuous subset of program space), and the AI proceeds to search for the solution, which is a program or routine that satisfies those constraints. As he explains,
A large portion of programmers of tomorrow do not maintain complex software repositories, write intricate programs, or analyze their running times. They collect, clean, manipulate, label, analyze and visualize data that feeds neural networks.
If you’ve followed what we do here, that may sound familiar. The network trains itself to make sense of the massive quantities of data made available to it, and render the desired result. In the Tesla’s case, they’re generating everything from software for visual recognition to speech synthesis and robotics.
That holds true even with the vehicles on the road. As Elon Musk, puts is, “The whole Tesla fleet operates as a network. When one car learns something, they all learn it.”
So what’s Marketing Software 2.0?
Much the same model holds when neural networks are put to work on behalf of account-based marketing (ABM. Digital marketing succeeds via personalization, and the ability to thinly slice segmentations down to the individual level, enabling 1:1 engagement, is the goal.
That demands analyzing prodigious amounts of data, and it can only be collected, collated and interpreted by non-human processes: the very neural networks and self-iterating machine learning processes Andrej Karpathy describes.
In the case of lead gen and ABM, a “Marketing Software 2.0” platform like ours lets the marketer skip the onerous stuff – finding out who and where their best leads are – and get down to the business of actual engagement, expedited by a precise knowledge of what channels, touchpoints, keywords, hot button concerns and other levers are suited to striking up a dialogue with that target.
The changes that Tesla’s lead AI wonk foresees for software in general are just as imminent and transformative when it comes to the programs and platforms used for digital marketing. Just see what some of its converts say about the effectiveness of AI-powered ABM.
Not just a promise anymore
The big difference between AI-powered autonomous automobiles and machine learning-powered marketing? One is still on the horizon, while the other is deployable in the here-and-now.
Our POV? Don’t wait too long to learn about the implications…and the opportunities. AI will be, literally, driving the next wave of automotive mobility, but it’s already driving bottom-line results for marketers smart enough to understand its potential not just tomorrow, but right now.